Shenzhen Academy of Metrology and Quality Inspection

This study investigated the prevalence of 11 common pathogens in dogs and cats in Shenzhen, China, from January 2022 to March 2024, aiming to enhance the understanding of their epidemiological characteristics for improved disease control strategies. Diagnostic testing for the target pathogens was performed using polymerase chain reaction (PCR), colloidal gold test strips, or fluorescence immunoassay. Statistical analysis revealed that among 13,134 cats, Feline Panleukopenia Virus (FPV) showed the highest prevalence (35.83%), followed by Feline Calicivirus (FCV, 26.20%), Feline Infectious Peritonitis Virus (FIPV, 22.00%), and Feline Herpesvirus (FHV, 15.76%). Among 3626 dogs, Canine Parvovirus (CPV) and Canine Distemper Virus (CDV) were predominant, showing a prevalence of 54.55% and 42.83%, respectively. Risk factor analysis showed that most infections occurred in unvaccinated animals and young individuals (<1 year old), with higher incidences in winter and spring. Logistic regression indicated that sex, age, and season were significantly associated with FPV, FHV, and FIPV infections, while age and season were associated with FCV, CPV, and CDV infections (sex showed no association). This study contributes to the epidemiological knowledge of common infectious diseases in dogs and cats, providing a theoretical basis for disease prevention in dogs and cats.

The digital transformation of industry, particularly in intelligent manufacturing and digital twin systems, demands high-quality, reliable data. Measurement uncertainty (MU) is the critical indicator for quantifying this data quality, yet traditional evaluation methods like the Guide to the Expression of Uncertainty in Measurement (GUM) and the Monte Carlo Method (MCM) face significant bottlenecks when applied to the complex, nonlinear systems inherent in modern digital environments. This paper, presented as a review article combined with a case study, systematically investigates the application of Artificial Intelligence (AI) in MU evaluation. We first conduct a systematic literature review, categorizing AI-assisted MU methods into two primary technical pathways: (i) AI-based surrogate modeling followed by GUM/MCM propagation, and (ii) intrinsic AI uncertainty quantification. A critical comparison is provided in terms of their numerical stability, interpretability, and metrological traceability. To ground the discussion in practice, this paper introduces a case study on the calibration of a strongly nonlinear Infra-Red Ear Thermometer (IRET), where GUM, conventional MCM, and a hybrid MCM approach driven by a Least-Squares Support Vector Machine (LS-SVM) are quantitatively evaluated. The results demonstrate that, compared to the MCM benchmark, the AIassisted route reduces computation time by nearly an order of magnitude while maintaining coverage probability, validating its significant potential for balancing accuracy and efficiency. Finally, this paper outlines future directions, including the development of a standardized community test platform (an “AI-MU Benchmark Suite”) and hybrid modeling strategies that combine physicsinformed principles with data-driven approaches, providing framework-like guidance for the reliable application of AI in digital metrology.

In fringe projection profilometry, system calibration is the key guarantee for three-dimensional reconstruction accuracy. Calibration residuals are usually used to evaluate system calibration accuracy. This paper quantitatively analyzes the impact of calibration residuals on the accuracy of 3D reconstruction, and constructs an uncertainty space for 3D reconstruction based on the camera ray model. Based on this, a high-precision three-dimensional imaging method utilizing the intersection of rays of four cameras is proposed. This method first uses an orthogonal four-step phase shift maps to solve the wrapped phase, and then solves the intersection of the corresponding rays of the four cameras to perform three-dimensional reconstruction. Compared with traditional stereo systems, this method has the significant advantages: it constrains the uncertainty space of three-dimensional reconstruction and improves measurement accuracy. Experimental results verify that this method can effectively improve the accuracy and speed of 3D reconstruction.

邻苯二甲酸酯(PAEs)因具有改善塑料柔软性和柔韧性的特性而被作为增塑剂广泛应用于各种食品包装材料,然而,其对人类健康的重大危害使得建立准确的分析方法显得至关重要。为了解决油性基质样品前处理费时费力的问题,我们开发了一种基于顶空固相微萃取(HS-SPME)结合气相色谱-质谱(GC-MS)检测的分析方法,用于快速检测植物油基质中的12种PAEs。将苄基化环糊精超交联聚合物(BnCD-HCP)涂敷在不锈钢纤维上制备了一种具有较大比表面积、良好稳定性好和较高重复性的固相微萃取探针,并对其结构、形貌进行了表征。将油脂样品以甲醇改性后以该探针顶空萃取PAEs,优化得到如下最佳萃取参数:萃取时间20 min,萃取温度50 ℃,解吸时间4 min,解吸温度275 ℃。在优化的分析条件下,12种目标PAEs在线性范围内具有良好的线性关系(相关系数(R2)均大于0.99),检出限和定量限分别为0.21~3.74 μg/kg和0.69~12.34 μg/kg,单针的相对标准偏差(RSD)≤11.4%,针间RSD≤13.9%。将此方法应用于大豆油、花生油和葵花油样品中PAEs的检测,加标试验结果表明该方法具有良好的精密度(RSD为1.17%~11.73)和回收率(72.49%~124.43%)。该研究建立的基于BnCD-HCP的HS-SPME方法不需要溶剂萃取和净化等繁琐操作,具有快速、灵敏、准确、环保等优点,为植物油中PAEs的灵敏筛检提供了新的方法和技术。

Due to the potential health risks of adulterated febuxostat in uric-acid-lowering foods, it is urgent to develop rapid detection methods. However, there are no fast analytical techniques for febuxostat yet. Herein, an efficient hapten simulation strategy was proposed to successfully produce a highly sensitive and selective monoclonal antibody toward febuxostat. Based on such a robust recognition element, easy colorimetric and ultrasensitive fluorescent lateral flow immunochromatographic immunoassays were first established, which can detect febuxostat as low as 60 μg/kg by the naked eye or 1.01 μg/kg by a commercial test strip reader with acceptable stability. Furthermore, in the recovery test and blind sample analysis, consistent results between our methods and the authorized liquid chromatography-tandem mass spectrometry method suggested the high accuracy and practicality of this work. The present work not only proposes a rational hapten design idea but also provides favorable tools for the rapid screening of febuxostat in functional foods.

Chlorphenamine maleate is a prohibited additive found in herbal teas and health foods. Excessive intake of this substance can result in adverse health effects. In this study, two novel haptens, PEM and bepotastine (PB1), mimicking chlorphenamine maleate structure were designed and synthesized based on molecular simulation for developing two corresponding polyclonal antibodies (PEM-Ab and PB1-Ab), respectively. Afterward, an indirect competitive enzyme-linked immunosorbent assay (ic-ELISA) was developed to quickly and accurately detect chlorphenamine maleate in herbal teas using PB1-Ab, which has a high sensitivity and specificity. For chlorphenamine maleate, the half-maximal inhibitory concentration (IC50) and limit of detection (LOD) of PB1-Ab under ideal circumstances were found to be 1.18 µg/L and 0.07 µg/L, respectively. Besides, an environmentally friendly sample pre-treatment strategy was employed that allowed easy and effective elimination of complex matrices. The ic-ELISA method observed the average recovery rate from 87.7% to 94.0% with the variance coefficient (CV) ranging from 2.2% to 9.4%. Additionally, the identification of 25 commercially available herbal teas using liquid chromatography-tandem mass spectrometry (LC-MS/MS) further confirmed the validity of our detection. The results of the two methods are consistent. Overall, the proposed ic-ELISA could be an ultrasensitive and reliable method for chlorphenamine maleate adulterated in foods or exposure to the environment.